Overview

Dataset statistics

Number of variables15
Number of observations138791
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.9 MiB
Average record size in memory120.0 B

Variable types

DateTime2
Numeric12
Categorical1

Alerts

Demanda is highly correlated with Month and 7 other fieldsHigh correlation
TMAX-CAB is highly correlated with Demanda and 7 other fieldsHigh correlation
TMAX-HMO is highly correlated with Demanda and 7 other fieldsHigh correlation
TMAX-OBR is highly correlated with Demanda and 7 other fieldsHigh correlation
TMIN-CAB is highly correlated with Demanda and 7 other fieldsHigh correlation
TMIN-HMO is highly correlated with Demanda and 7 other fieldsHigh correlation
TMIN-OBR is highly correlated with Demanda and 7 other fieldsHigh correlation
Month is highly correlated with Demanda and 7 other fieldsHigh correlation
Season is highly correlated with Demanda and 7 other fieldsHigh correlation
Date_time has unique values Unique
Date has 19847 (14.3%) zeros Zeros
Hour has 5782 (4.2%) zeros Zeros
PREC_HMO (mm) has 120839 (87.1%) zeros Zeros
PREC_OBR (mm) has 122255 (88.1%) zeros Zeros

Reproduction

Analysis started2022-11-08 15:58:32.925716
Analysis finished2022-11-08 15:59:11.024482
Duration38.1 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

FECHA
Date

Distinct5783
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2007-01-01 00:00:00
Maximum2022-10-31 00:00:00
2022-11-08T08:59:11.094556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:11.177461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Demanda
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45638
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2505.570239
Minimum959
Maximum5402.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:11.273428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum959
5-th percentile1408
Q11867
median2343.74
Q33054.1
95-th percentile4079
Maximum5402.72
Range4443.72
Interquartile range (IQR)1187.1

Descriptive statistics

Standard deviation823.7167817
Coefficient of variation (CV)0.3287542168
Kurtosis-0.2876228818
Mean2505.570239
Median Absolute Deviation (MAD)559.74
Skewness0.6406278244
Sum347750599
Variance678509.3364
MonotonicityNot monotonic
2022-11-08T08:59:11.356451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160082
 
0.1%
178680
 
0.1%
151578
 
0.1%
149776
 
0.1%
157472
 
0.1%
178472
 
0.1%
153772
 
0.1%
157872
 
0.1%
181271
 
0.1%
159071
 
0.1%
Other values (45628)138045
99.5%
ValueCountFrequency (%)
9591
< 0.1%
9661
< 0.1%
9801
< 0.1%
9991
< 0.1%
10011
< 0.1%
10021
< 0.1%
10032
< 0.1%
10041
< 0.1%
10051
< 0.1%
10061
< 0.1%
ValueCountFrequency (%)
5402.721
< 0.1%
53991
< 0.1%
53901
< 0.1%
5348.841
< 0.1%
5336.531
< 0.1%
5297.231
< 0.1%
5295.021
< 0.1%
5290.241
< 0.1%
5289.391
< 0.1%
52861
< 0.1%

Date_time
Date

UNIQUE

Distinct138791
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2007-01-01 01:00:00
Maximum2022-10-31 23:00:00
2022-11-08T08:59:11.443717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:11.524761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Date
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.99950285
Minimum0
Maximum6
Zeros19847
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:11.600484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.000214279
Coefficient of variation (CV)0.6668486011
Kurtosis-1.250177853
Mean2.99950285
Median Absolute Deviation (MAD)2
Skewness0.0001865299127
Sum416304
Variance4.000857163
MonotonicityNot monotonic
2022-11-08T08:59:11.663182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
019847
14.3%
119824
14.3%
219824
14.3%
319824
14.3%
419824
14.3%
519824
14.3%
619824
14.3%
ValueCountFrequency (%)
019847
14.3%
119824
14.3%
219824
14.3%
319824
14.3%
419824
14.3%
519824
14.3%
619824
14.3%
ValueCountFrequency (%)
619824
14.3%
519824
14.3%
419824
14.3%
319824
14.3%
219824
14.3%
119824
14.3%
019847
14.3%

Hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.50008286
Minimum0
Maximum23
Zeros5782
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:11.740892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q317.5
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.922167599
Coefficient of variation (CV)0.6019232804
Kurtosis-1.204170464
Mean11.50008286
Median Absolute Deviation (MAD)6
Skewness-2.872424259 × 10-6
Sum1596108
Variance47.91640427
MonotonicityNot monotonic
2022-11-08T08:59:11.800811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15783
 
4.2%
25783
 
4.2%
235783
 
4.2%
225783
 
4.2%
215783
 
4.2%
205783
 
4.2%
195783
 
4.2%
185783
 
4.2%
175783
 
4.2%
165783
 
4.2%
Other values (14)80961
58.3%
ValueCountFrequency (%)
05782
4.2%
15783
4.2%
25783
4.2%
35783
4.2%
45783
4.2%
55783
4.2%
65783
4.2%
75783
4.2%
85783
4.2%
95783
4.2%
ValueCountFrequency (%)
235783
4.2%
225783
4.2%
215783
4.2%
205783
4.2%
195783
4.2%
185783
4.2%
175783
4.2%
165783
4.2%
155783
4.2%
145783
4.2%

Month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.470383526
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:11.864179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.428032772
Coefficient of variation (CV)0.5298036443
Kurtosis-1.196339534
Mean6.470383526
Median Absolute Deviation (MAD)3
Skewness0.003035689015
Sum898031
Variance11.75140869
MonotonicityNot monotonic
2022-11-08T08:59:11.915709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
311904
8.6%
511904
8.6%
711904
8.6%
811904
8.6%
1011904
8.6%
111903
8.6%
411520
8.3%
611520
8.3%
911520
8.3%
1211160
8.0%
Other values (2)21648
15.6%
ValueCountFrequency (%)
111903
8.6%
210848
7.8%
311904
8.6%
411520
8.3%
511904
8.6%
611520
8.3%
711904
8.6%
811904
8.6%
911520
8.3%
1011904
8.6%
ValueCountFrequency (%)
1211160
8.0%
1110800
7.8%
1011904
8.6%
911520
8.3%
811904
8.6%
711904
8.6%
611520
8.3%
511904
8.6%
411520
8.3%
311904
8.6%

Season
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Spring
35328 
Summer
35328 
Autumn
34224 
Winter
33911 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters832746
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Spring35328
25.5%
Summer35328
25.5%
Autumn34224
24.7%
Winter33911
24.4%

Length

2022-11-08T08:59:11.979739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-08T08:59:12.063169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
spring35328
25.5%
summer35328
25.5%
autumn34224
24.7%
winter33911
24.4%

Most occurring characters

ValueCountFrequency (%)
m104880
12.6%
r104567
12.6%
u103776
12.5%
n103463
12.4%
S70656
8.5%
i69239
8.3%
e69239
8.3%
t68135
8.2%
p35328
 
4.2%
g35328
 
4.2%
Other values (2)68135
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter693955
83.3%
Uppercase Letter138791
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m104880
15.1%
r104567
15.1%
u103776
15.0%
n103463
14.9%
i69239
10.0%
e69239
10.0%
t68135
9.8%
p35328
 
5.1%
g35328
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
S70656
50.9%
A34224
24.7%
W33911
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin832746
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m104880
12.6%
r104567
12.6%
u103776
12.5%
n103463
12.4%
S70656
8.5%
i69239
8.3%
e69239
8.3%
t68135
8.2%
p35328
 
4.2%
g35328
 
4.2%
Other values (2)68135
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII832746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m104880
12.6%
r104567
12.6%
u103776
12.5%
n103463
12.4%
S70656
8.5%
i69239
8.3%
e69239
8.3%
t68135
8.2%
p35328
 
4.2%
g35328
 
4.2%
Other values (2)68135
8.2%

TMAX-CAB
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2134
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.99878551
Minimum9.09
Maximum49.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:12.152302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.09
5-th percentile19.8
Q126.96
median33.73
Q339.5
95-th percentile44.06
Maximum49.61
Range40.52
Interquartile range (IQR)12.54

Descriptive statistics

Standard deviation7.801089285
Coefficient of variation (CV)0.2364053454
Kurtosis-0.8615582407
Mean32.99878551
Median Absolute Deviation (MAD)6.27
Skewness-0.2962016899
Sum4579934.44
Variance60.85699404
MonotonicityNot monotonic
2022-11-08T08:59:12.249241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
401392
 
1.0%
381248
 
0.9%
411200
 
0.9%
261128
 
0.8%
391128
 
0.8%
361104
 
0.8%
231080
 
0.8%
311056
 
0.8%
251056
 
0.8%
371008
 
0.7%
Other values (2124)127391
91.8%
ValueCountFrequency (%)
9.0924
 
< 0.1%
1124
 
< 0.1%
1272
0.1%
12.0924
 
< 0.1%
12.2324
 
< 0.1%
12.324
 
< 0.1%
12.524
 
< 0.1%
12.8124
 
< 0.1%
1372
0.1%
13.124
 
< 0.1%
ValueCountFrequency (%)
49.6124
< 0.1%
49.5324
< 0.1%
48.424
< 0.1%
48.124
< 0.1%
4824
< 0.1%
47.8724
< 0.1%
47.7124
< 0.1%
47.6924
< 0.1%
47.624
< 0.1%
47.5324
< 0.1%

TMAX-HMO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1328
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.55376026
Minimum8
Maximum49.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:12.352081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile21.77
Q128.71
median34.5
Q339
95-th percentile43
Maximum49.1
Range41.1
Interquartile range (IQR)10.29

Descriptive statistics

Standard deviation6.634450905
Coefficient of variation (CV)0.1977260031
Kurtosis-0.6405264241
Mean33.55376026
Median Absolute Deviation (MAD)5
Skewness-0.3834708529
Sum4656959.94
Variance44.01593881
MonotonicityNot monotonic
2022-11-08T08:59:12.445091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
373888
 
2.8%
393408
 
2.5%
383336
 
2.4%
353336
 
2.4%
403192
 
2.3%
413144
 
2.3%
362760
 
2.0%
422760
 
2.0%
342664
 
1.9%
302568
 
1.9%
Other values (1318)107735
77.6%
ValueCountFrequency (%)
824
 
< 0.1%
14.5824
 
< 0.1%
14.824
 
< 0.1%
15.548
 
< 0.1%
15.724
 
< 0.1%
15.7124
 
< 0.1%
1696
0.1%
16.0124
 
< 0.1%
16.448
 
< 0.1%
16.5144
0.1%
ValueCountFrequency (%)
49.124
 
< 0.1%
48.8624
 
< 0.1%
4824
 
< 0.1%
47.5824
 
< 0.1%
47.572
0.1%
4796
0.1%
46.8624
 
< 0.1%
46.5120
0.1%
46.2324
 
< 0.1%
46.224
 
< 0.1%

TMAX-OBR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1243
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.53774279
Minimum12
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:12.548549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile24
Q130
median35.41
Q339.28
95-th percentile43
Maximum47
Range35
Interquartile range (IQR)9.28

Descriptive statistics

Standard deviation5.927637647
Coefficient of variation (CV)0.1716278242
Kurtosis-0.649182679
Mean34.53774279
Median Absolute Deviation (MAD)4.59
Skewness-0.4153157434
Sum4793527.86
Variance35.13688808
MonotonicityNot monotonic
2022-11-08T08:59:12.654653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405856
 
4.2%
385568
 
4.0%
395424
 
3.9%
414992
 
3.6%
374704
 
3.4%
364320
 
3.1%
423792
 
2.7%
293744
 
2.7%
343648
 
2.6%
333600
 
2.6%
Other values (1233)93143
67.1%
ValueCountFrequency (%)
1224
 
< 0.1%
1524
 
< 0.1%
1648
 
< 0.1%
1796
0.1%
17.724
 
< 0.1%
1896
0.1%
18.524
 
< 0.1%
18.8324
 
< 0.1%
19168
0.1%
19.224
 
< 0.1%
ValueCountFrequency (%)
4748
 
< 0.1%
46.524
 
< 0.1%
46.1524
 
< 0.1%
46168
0.1%
45.9724
 
< 0.1%
45.8524
 
< 0.1%
45.724
 
< 0.1%
45.6824
 
< 0.1%
45.548
 
< 0.1%
45.4524
 
< 0.1%

TMIN-CAB
Real number (ℝ)

HIGH CORRELATION

Distinct2169
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.49558574
Minimum-6.8
Maximum32.9
Zeros192
Zeros (%)0.1%
Negative720
Negative (%)0.5%
Memory size1.1 MiB
2022-11-08T08:59:12.748474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-6.8
5-th percentile4
Q110.1
median16
Q323.57
95-th percentile28.78
Maximum32.9
Range39.7
Interquartile range (IQR)13.47

Descriptive statistics

Standard deviation7.921575778
Coefficient of variation (CV)0.4802239766
Kurtosis-1.016973919
Mean16.49558574
Median Absolute Deviation (MAD)6.64
Skewness-0.0117286117
Sum2289438.84
Variance62.75136281
MonotonicityNot monotonic
2022-11-08T08:59:12.837357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221440
 
1.0%
121440
 
1.0%
241368
 
1.0%
101248
 
0.9%
81224
 
0.9%
141200
 
0.9%
91176
 
0.8%
151151
 
0.8%
231104
 
0.8%
261080
 
0.8%
Other values (2159)126360
91.0%
ValueCountFrequency (%)
-6.824
< 0.1%
-5.824
< 0.1%
-5.424
< 0.1%
-524
< 0.1%
-3.224
< 0.1%
-348
< 0.1%
-2.9224
< 0.1%
-2.224
< 0.1%
-2.1324
< 0.1%
-248
< 0.1%
ValueCountFrequency (%)
32.924
< 0.1%
32.8624
< 0.1%
32.524
< 0.1%
32.4824
< 0.1%
32.3624
< 0.1%
32.2124
< 0.1%
32.0624
< 0.1%
32.0224
< 0.1%
32.0124
< 0.1%
31.7824
< 0.1%

TMIN-HMO
Real number (ℝ)

HIGH CORRELATION

Distinct1338
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.50379463
Minimum-3
Maximum34
Zeros24
Zeros (%)< 0.1%
Negative24
Negative (%)< 0.1%
Memory size1.1 MiB
2022-11-08T08:59:12.958273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile7.09
Q113
median18
Q325
95-th percentile29
Maximum34
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.05737714
Coefficient of variation (CV)0.3814016143
Kurtosis-1.049131135
Mean18.50379463
Median Absolute Deviation (MAD)6
Skewness-0.1101890143
Sum2568160.16
Variance49.8065721
MonotonicityNot monotonic
2022-11-08T08:59:13.067592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
274344
 
3.1%
263624
 
2.6%
253600
 
2.6%
113240
 
2.3%
163239
 
2.3%
283120
 
2.2%
133000
 
2.2%
142952
 
2.1%
222880
 
2.1%
122784
 
2.0%
Other values (1328)106008
76.4%
ValueCountFrequency (%)
-324
< 0.1%
024
< 0.1%
0.3724
< 0.1%
0.4624
< 0.1%
0.6224
< 0.1%
0.6624
< 0.1%
0.6824
< 0.1%
0.724
< 0.1%
1.0624
< 0.1%
1.2324
< 0.1%
ValueCountFrequency (%)
3424
 
< 0.1%
33.424
 
< 0.1%
33.324
 
< 0.1%
32168
 
0.1%
31.824
 
< 0.1%
31.7324
 
< 0.1%
31.548
 
< 0.1%
31.3924
 
< 0.1%
31.0724
 
< 0.1%
31720
0.5%

TMIN-OBR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1304
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.66678272
Minimum2
Maximum42.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:13.150461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.22
Q113
median18
Q325
95-th percentile29
Maximum42.5
Range40.5
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.737229591
Coefficient of variation (CV)0.36092077
Kurtosis-1.139080852
Mean18.66678272
Median Absolute Deviation (MAD)6
Skewness0.02303209835
Sum2590781.44
Variance45.39026256
MonotonicityNot monotonic
2022-11-08T08:59:13.229743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265328
 
3.8%
254776
 
3.4%
144559
 
3.3%
274464
 
3.2%
244440
 
3.2%
104128
 
3.0%
133984
 
2.9%
163936
 
2.8%
153864
 
2.8%
113792
 
2.7%
Other values (1294)95520
68.8%
ValueCountFrequency (%)
248
< 0.1%
2.624
< 0.1%
324
< 0.1%
3.324
< 0.1%
3.8824
< 0.1%
448
< 0.1%
4.0424
< 0.1%
4.124
< 0.1%
4.3424
< 0.1%
4.4824
< 0.1%
ValueCountFrequency (%)
42.524
 
< 0.1%
3324
 
< 0.1%
32144
0.1%
31.9624
 
< 0.1%
31.9224
 
< 0.1%
31.6724
 
< 0.1%
31.6224
 
< 0.1%
31.5624
 
< 0.1%
31.5124
 
< 0.1%
31.4124
 
< 0.1%

PREC_HMO (mm)
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.398324099
Minimum0
Maximum117
Zeros120839
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:13.730861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7.2
Maximum117
Range117
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.939963432
Coefficient of variation (CV)4.963057876
Kurtosis76.87536046
Mean1.398324099
Median Absolute Deviation (MAD)0
Skewness7.721436168
Sum194074.8
Variance48.16309244
MonotonicityNot monotonic
2022-11-08T08:59:13.816048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0120839
87.1%
0.013312
 
2.4%
1480
 
0.3%
0.3384
 
0.3%
1.2360
 
0.3%
0.5336
 
0.2%
0.1312
 
0.2%
2288
 
0.2%
0.2288
 
0.2%
3264
 
0.2%
Other values (242)11928
 
8.6%
ValueCountFrequency (%)
0120839
87.1%
0.013312
 
2.4%
0.0424
 
< 0.1%
0.1312
 
0.2%
0.2288
 
0.2%
0.3384
 
0.3%
0.4192
 
0.1%
0.5336
 
0.2%
0.696
 
0.1%
0.7144
 
0.1%
ValueCountFrequency (%)
11724
< 0.1%
11524
< 0.1%
103.924
< 0.1%
92.124
< 0.1%
83.524
< 0.1%
80.524
< 0.1%
8024
< 0.1%
72.524
< 0.1%
7224
< 0.1%
71.824
< 0.1%

PREC_OBR (mm)
Real number (ℝ≥0)

ZEROS

Distinct184
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.166726373
Minimum0
Maximum166.8
Zeros122255
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-11-08T08:59:13.912236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.5
Maximum166.8
Range166.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.680982958
Coefficient of variation (CV)5.726263771
Kurtosis141.1251942
Mean1.166726373
Median Absolute Deviation (MAD)0
Skewness10.03783287
Sum161931.12
Variance44.63553329
MonotonicityNot monotonic
2022-11-08T08:59:13.987218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0122255
88.1%
0.012856
 
2.1%
11440
 
1.0%
0.5936
 
0.7%
2720
 
0.5%
1.5552
 
0.4%
3480
 
0.3%
2.5384
 
0.3%
5336
 
0.2%
9264
 
0.2%
Other values (174)8568
 
6.2%
ValueCountFrequency (%)
0122255
88.1%
0.012856
 
2.1%
0.0524
 
< 0.1%
0.1144
 
0.1%
0.2216
 
0.2%
0.3240
 
0.2%
0.424
 
< 0.1%
0.5936
 
0.7%
0.696
 
0.1%
0.724
 
< 0.1%
ValueCountFrequency (%)
166.824
< 0.1%
10424
< 0.1%
10224
< 0.1%
10124
< 0.1%
91.724
< 0.1%
9124
< 0.1%
8524
< 0.1%
8124
< 0.1%
80.624
< 0.1%
7424
< 0.1%

Interactions

2022-11-08T08:59:09.344241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.115947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.239207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.461951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:00.837497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.834599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.922474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.943662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.974214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.027085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.254806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.311279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.429359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.227504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.325085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.547419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:00.915970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.918664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.999274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.025932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.061311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.114003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.341125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.395742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.513134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.331672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.423601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.623611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:00.993814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.013077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.084370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.109552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.143919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.198408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.426303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.480210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.601274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.423633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.508390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.701292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.073005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.094617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.165286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.190518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.227349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.279571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.510021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.560241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.689019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.513171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.603468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.778889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.153420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.181683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.246417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.277317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.315521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.364849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.597671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.647806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.781757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.608038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.710348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.861988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.235370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.282317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.339785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.366124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.406782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.448075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.687556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.738306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.864881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.689391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.818348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.942195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.315108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.363992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.415927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.444014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.492622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.729427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.770296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.819886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.953932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.775039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.927975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:00.352713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.401288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.452776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.501895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.535808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.582914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.814691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.860649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.909081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:10.057629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.868834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.024881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:00.449918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.492390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.552383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.588837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.619327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.668281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.900692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.947687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.992734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:10.145103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:57.968255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.119847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:00.545681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.578098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.642914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.677840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.705626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.759165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:06.988800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.035833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.079713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:10.237317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.065228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.243447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:00.652026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.664013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.735789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.771533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.797863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.850379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.081707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.130271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.173004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:10.324859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:58.154138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:58:59.361529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:00.749803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:01.750338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:02.823969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:03.859691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:04.883279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:05.935746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:07.166673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:08.217424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T08:59:09.257823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-08T08:59:14.063385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-08T08:59:14.167144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-08T08:59:14.272716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-08T08:59:14.377392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-08T08:59:10.463209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-08T08:59:10.753562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FECHADemandaDate_timeDateHourMonthSeasonTMAX-CABTMAX-HMOTMAX-OBRTMIN-CABTMIN-HMOTMIN-OBRPREC_HMO (mm)PREC_OBR (mm)
02007-01-011297.02007-01-01 01:00:00011Winter21.021.525.02.09.07.50.00.0
12007-01-011255.02007-01-01 02:00:00021Winter21.021.525.02.09.07.50.00.0
22007-01-011222.02007-01-01 03:00:00031Winter21.021.525.02.09.07.50.00.0
32007-01-011168.02007-01-01 04:00:00041Winter21.021.525.02.09.07.50.00.0
42007-01-011128.02007-01-01 05:00:00051Winter21.021.525.02.09.07.50.00.0
52007-01-011100.02007-01-01 06:00:00061Winter21.021.525.02.09.07.50.00.0
62007-01-011083.02007-01-01 07:00:00071Winter21.021.525.02.09.07.50.00.0
72007-01-011076.02007-01-01 08:00:00081Winter21.021.525.02.09.07.50.00.0
82007-01-011022.02007-01-01 09:00:00091Winter21.021.525.02.09.07.50.00.0
92007-01-011029.02007-01-01 10:00:000101Winter21.021.525.02.09.07.50.00.0

Last rows

FECHADemandaDate_timeDateHourMonthSeasonTMAX-CABTMAX-HMOTMAX-OBRTMIN-CABTMIN-HMOTMIN-OBRPREC_HMO (mm)PREC_OBR (mm)
1387812022-10-312834.872022-10-31 14:00:0001410Autumn29.031.531.515.016.014.00.00.0
1387822022-10-312914.072022-10-31 15:00:0001510Autumn29.031.531.515.016.014.00.00.0
1387832022-10-312990.752022-10-31 16:00:0001610Autumn29.031.531.515.016.014.00.00.0
1387842022-10-313038.082022-10-31 17:00:0001710Autumn29.031.531.515.016.014.00.00.0
1387852022-10-313014.082022-10-31 18:00:0001810Autumn29.031.531.515.016.014.00.00.0
1387862022-10-312934.742022-10-31 19:00:0001910Autumn29.031.531.515.016.014.00.00.0
1387872022-10-312926.852022-10-31 20:00:0002010Autumn29.031.531.515.016.014.00.00.0
1387882022-10-312894.382022-10-31 21:00:0002110Autumn29.031.531.515.016.014.00.00.0
1387892022-10-312877.562022-10-31 22:00:0002210Autumn29.031.531.515.016.014.00.00.0
1387902022-10-312843.082022-10-31 23:00:0002310Autumn29.031.531.515.016.014.00.00.0